Universität zu Köln

Rozhonova, Hana, Danciu, Daniel, Stark, Stefan ORCID: 0000-0003-2478-9512, Raetsch, Gunnar, Kahles, Andre ORCID: 0000-0002-3411-0692 and Lehmann, Kjong-Van ORCID: 0000-0002-1936-298X (2022). SECEDO: SNV-based subclone detection using ultra-low coverage single-cell DNA sequencing. Bioinformatics, 38 (18). S. 4293 - 4301. OXFORD: OXFORD UNIV PRESS. ISSN 1460-2059

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Abstract

Motivation: Several recently developed single-cell DNA sequencing technologies enable whole-genome sequencing of thousands of cells. However, the ultra-low coverage of the sequenced data (<0.05x per cell) mostly limits their usage to the identification of copy number alterations in multi-megabase segments. Many tumors are not copy number-driven, and thus single-nucleotide variant (SNV)-based subclone detection may contribute to a more comprehensive view on intra-tumor heterogeneity. Due to the low coverage of the data, the identification of SNVs is only possible when superimposing the sequenced genomes of hundreds of genetically similar cells. Thus, we have developed a new approach to efficiently cluster tumor cells based on a Bayesian filtering approach of relevant loci and exploiting read overlap and phasing. Results: We developed Single Cell Data Tumor Clusterer (SECEDO, lat. 'to separate'), a new method to cluster tumor cells based solely on SNVs, inferred on ultra-low coverage single-cell DNA sequencing data. We applied SECEDO to a synthetic dataset simulating 7250 cells and eight tumor subclones from a single patient and were able to accurately reconstruct the clonal composition, detecting 92.11% of the somatic SNVs, with the smallest clusters representing only 6.9% of the total population. When applied to five real single-cell sequencing datasets from a breast cancer patient, each consisting of approximate to 2000 cells, SECEDO was able to recover the major clonal composition in each dataset at the original coverage of 0.03x, achieving an Adjusted Rand Index (ARI) score of approximate to 0.6. The current state-of-the-art SNV-based clustering method achieved an ARI score of approximate to 0, even after merging cells to create higher coverage data (factor 10 increase), and was only able to match SECEDOs performance when pooling data from all five datasets, in addition to artificially increasing the sequencing coverage by a factor of 7. Variant calling on the resulting clusters recovered more than twice as many SNVs as would have been detected if calling on all cells together. Further, the allelic ratio of the called SNVs on each subcluster was more than double relative to the allelic ratio of the SNVs called without clustering, thus demonstrating that calling variants on subclones, in addition to both increasing sensitivity of SNV detection and attaching SNVs to subclones, significantly increases the confidence of the called variants. Availability and implementation: SECEDO is implemented in Cthornthorn and is publicly available at https://github.com/rats chlab/secedo. Instructions to download the data and the evaluation code to reproduce the findings in this paper are available at: https://github.com/ratschlab/secedo-evaluation. The code and data of the submitted version are archived at: https://doi.org/10.5281/zenodo.6516955. Contact: kjlehmann@ukaachen.de Supplementary information: Supplementary data are available at Bioinformatics online.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Rozhonova, Hana UNSPECIFIED UNSPECIFIED UNSPECIFIED Danciu, Daniel UNSPECIFIED UNSPECIFIED UNSPECIFIED Stark, Stefan UNSPECIFIED orcid.org/0000-0003-2478-9512 UNSPECIFIED Raetsch, Gunnar UNSPECIFIED UNSPECIFIED UNSPECIFIED Kahles, Andre UNSPECIFIED orcid.org/0000-0002-3411-0692 UNSPECIFIED Lehmann, Kjong-Van UNSPECIFIED orcid.org/0000-0002-1936-298X UNSPECIFIED
URN:	urn:nbn:de:hbz:38-686102
DOI:	10.1093/bioinformatics/btac510
Journal or Publication Title:	Bioinformatics
Volume:	38
Number:	18
Page Range:	S. 4293 - 4301
Date:	2022
Publisher:	OXFORD UNIV PRESS
Place of Publication:	OXFORD
ISSN:	1460-2059
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language VARIANT DETECTION; TUMOR EVOLUTION; CANCER; GENOME; HETEROGENEITY; DISCOVERY; FRAMEWORK; ACCURATE Multiple languages Biochemical Research Methods; Biotechnology & Applied Microbiology; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Statistics & Probability Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/68610